{
 "cells": [
  {
   "cell_type": "markdown",
   "source": [
    "###### seaborn.set(): 设置风格和参数"
   ],
   "metadata": {
    "collapsed": false,
    "pycharm": {
     "name": "#%% md\n"
    }
   }
  },
  {
   "cell_type": "code",
   "execution_count": 93,
   "metadata": {
    "collapsed": true,
    "pycharm": {
     "name": "#%%\n"
    }
   },
   "outputs": [],
   "source": [
    "import seaborn as sns\n",
    "import matplotlib.pyplot as plt"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 94,
   "outputs": [
    {
     "data": {
      "text/plain": "     total_bill   tip     sex smoker   day    time  size\n0         16.99  1.01  Female     No   Sun  Dinner     2\n1         10.34  1.66    Male     No   Sun  Dinner     3\n2         21.01  3.50    Male     No   Sun  Dinner     3\n3         23.68  3.31    Male     No   Sun  Dinner     2\n4         24.59  3.61  Female     No   Sun  Dinner     4\n..          ...   ...     ...    ...   ...     ...   ...\n239       29.03  5.92    Male     No   Sat  Dinner     3\n240       27.18  2.00  Female    Yes   Sat  Dinner     2\n241       22.67  2.00    Male    Yes   Sat  Dinner     2\n242       17.82  1.75    Male     No   Sat  Dinner     2\n243       18.78  3.00  Female     No  Thur  Dinner     2\n\n[244 rows x 7 columns]",
      "text/html": "<div>\n<style scoped>\n    .dataframe tbody tr th:only-of-type {\n        vertical-align: middle;\n    }\n\n    .dataframe tbody tr th {\n        vertical-align: top;\n    }\n\n    .dataframe thead th {\n        text-align: right;\n    }\n</style>\n<table border=\"1\" class=\"dataframe\">\n  <thead>\n    <tr style=\"text-align: right;\">\n      <th></th>\n      <th>total_bill</th>\n      <th>tip</th>\n      <th>sex</th>\n      <th>smoker</th>\n      <th>day</th>\n      <th>time</th>\n      <th>size</th>\n    </tr>\n  </thead>\n  <tbody>\n    <tr>\n      <th>0</th>\n      <td>16.99</td>\n      <td>1.01</td>\n      <td>Female</td>\n      <td>No</td>\n      <td>Sun</td>\n      <td>Dinner</td>\n      <td>2</td>\n    </tr>\n    <tr>\n      <th>1</th>\n      <td>10.34</td>\n      <td>1.66</td>\n      <td>Male</td>\n      <td>No</td>\n      <td>Sun</td>\n      <td>Dinner</td>\n      <td>3</td>\n    </tr>\n    <tr>\n      <th>2</th>\n      <td>21.01</td>\n      <td>3.50</td>\n      <td>Male</td>\n      <td>No</td>\n      <td>Sun</td>\n      <td>Dinner</td>\n      <td>3</td>\n    </tr>\n    <tr>\n      <th>3</th>\n      <td>23.68</td>\n      <td>3.31</td>\n      <td>Male</td>\n      <td>No</td>\n      <td>Sun</td>\n      <td>Dinner</td>\n      <td>2</td>\n    </tr>\n    <tr>\n      <th>4</th>\n      <td>24.59</td>\n      <td>3.61</td>\n      <td>Female</td>\n      <td>No</td>\n      <td>Sun</td>\n      <td>Dinner</td>\n      <td>4</td>\n    </tr>\n    <tr>\n      <th>...</th>\n      <td>...</td>\n      <td>...</td>\n      <td>...</td>\n      <td>...</td>\n      <td>...</td>\n      <td>...</td>\n      <td>...</td>\n    </tr>\n    <tr>\n      <th>239</th>\n      <td>29.03</td>\n      <td>5.92</td>\n      <td>Male</td>\n      <td>No</td>\n      <td>Sat</td>\n      <td>Dinner</td>\n      <td>3</td>\n    </tr>\n    <tr>\n      <th>240</th>\n      <td>27.18</td>\n      <td>2.00</td>\n      <td>Female</td>\n      <td>Yes</td>\n      <td>Sat</td>\n      <td>Dinner</td>\n      <td>2</td>\n    </tr>\n    <tr>\n      <th>241</th>\n      <td>22.67</td>\n      <td>2.00</td>\n      <td>Male</td>\n      <td>Yes</td>\n      <td>Sat</td>\n      <td>Dinner</td>\n      <td>2</td>\n    </tr>\n    <tr>\n      <th>242</th>\n      <td>17.82</td>\n      <td>1.75</td>\n      <td>Male</td>\n      <td>No</td>\n      <td>Sat</td>\n      <td>Dinner</td>\n      <td>2</td>\n    </tr>\n    <tr>\n      <th>243</th>\n      <td>18.78</td>\n      <td>3.00</td>\n      <td>Female</td>\n      <td>No</td>\n      <td>Thur</td>\n      <td>Dinner</td>\n      <td>2</td>\n    </tr>\n  </tbody>\n</table>\n<p>244 rows × 7 columns</p>\n</div>"
     },
     "execution_count": 94,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df = sns.load_dataset('tips')\n",
    "df"
   ],
   "metadata": {
    "collapsed": false,
    "pycharm": {
     "name": "#%%\n"
    }
   }
  },
  {
   "cell_type": "code",
   "execution_count": 95,
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "<class 'pandas.core.frame.DataFrame'>\n",
      "RangeIndex: 244 entries, 0 to 243\n",
      "Data columns (total 7 columns):\n",
      " #   Column      Non-Null Count  Dtype   \n",
      "---  ------      --------------  -----   \n",
      " 0   total_bill  244 non-null    float64 \n",
      " 1   tip         244 non-null    float64 \n",
      " 2   sex         244 non-null    category\n",
      " 3   smoker      244 non-null    category\n",
      " 4   day         244 non-null    category\n",
      " 5   time        244 non-null    category\n",
      " 6   size        244 non-null    int64   \n",
      "dtypes: category(4), float64(2), int64(1)\n",
      "memory usage: 7.3 KB\n"
     ]
    }
   ],
   "source": [
    "# 0   total_bill  244 non-null    float64 \n",
    "#  1   tip         244 non-null    float64 \n",
    "df.info()"
   ],
   "metadata": {
    "collapsed": false,
    "pycharm": {
     "name": "#%%\n"
    }
   }
  },
  {
   "cell_type": "code",
   "execution_count": 96,
   "outputs": [
    {
     "data": {
      "text/plain": "total_bill    229\ntip           123\nsex             2\nsmoker          2\nday             4\ntime            2\nsize            6\ndtype: int64"
     },
     "execution_count": 96,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# 查看每一行的唯一值。如果比较少，可以将其当作分类类型\n",
    "df.apply(lambda s: len(s.unique()))"
   ],
   "metadata": {
    "collapsed": false,
    "pycharm": {
     "name": "#%%\n"
    }
   }
  },
  {
   "cell_type": "code",
   "execution_count": 97,
   "outputs": [],
   "source": [
    "\n",
    "sns.set(\n",
    "    # context='talk', # {paper, notebook, talk, poster} 字体比例\n",
    "    style='whitegrid', # {darkgrid, whitegrid, dark, white, ticks}\n",
    "    palette='muted', # deep, muted, bright, pastel, dark, colorblind,hls, husl, cmap, [colors] 调色板\n",
    "    font_scale=1.3, # 字体缩放参数 和context='talk'一样\n",
    "    rc={ # 默认参数，传入一个字典\n",
    "      'figure.facecolor': 'yellow',\n",
    "      'font.sans-serif': ['SimHei']  # ['Hiragino Sans GB'] \n",
    "    }\n",
    ")"
   ],
   "metadata": {
    "collapsed": false,
    "pycharm": {
     "name": "#%%\n"
    }
   }
  },
  {
   "cell_type": "code",
   "execution_count": 98,
   "outputs": [],
   "source": [
    "# plt.rcParams"
   ],
   "metadata": {
    "collapsed": false,
    "pycharm": {
     "name": "#%%\n"
    }
   }
  },
  {
   "cell_type": "code",
   "execution_count": 99,
   "outputs": [
    {
     "data": {
      "text/plain": "<matplotlib.axes._subplots.AxesSubplot at 0x7fd2a88e31d0>"
     },
     "execution_count": 99,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "text/plain": "<Figure size 432x288 with 1 Axes>",
      "image/png": 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\n"
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "sns.scatterplot(\n",
    "    x='total_bill', # x和y都是data=df的列名\n",
    "    y='tip',\n",
    "    hue='day',\n",
    "    data=df\n",
    ")"
   ],
   "metadata": {
    "collapsed": false,
    "pycharm": {
     "name": "#%%\n"
    }
   }
  },
  {
   "cell_type": "code",
   "execution_count": 100,
   "outputs": [
    {
     "data": {
      "text/plain": "Text(0.5, 0, '周几')"
     },
     "execution_count": 100,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "text/plain": "<Figure size 432x288 with 1 Axes>",
      "image/png": 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\n"
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "ax = sns.barplot(\n",
    "    x='day', # 分类类型\n",
    "    y='total_bill',\n",
    "    hue='sex', # 性别只有两种颜色\n",
    "    data=df\n",
    ")\n",
    "ax.set_xlabel('周几')"
   ],
   "metadata": {
    "collapsed": false,
    "pycharm": {
     "name": "#%%\n"
    }
   }
  },
  {
   "cell_type": "code",
   "execution_count": 100,
   "outputs": [],
   "source": [],
   "metadata": {
    "collapsed": false,
    "pycharm": {
     "name": "#%%\n"
    }
   }
  }
 ],
 "metadata": {
  "kernelspec": {
   "display_name": "Python 3",
   "language": "python",
   "name": "python3"
  },
  "language_info": {
   "codemirror_mode": {
    "name": "ipython",
    "version": 2
   },
   "file_extension": ".py",
   "mimetype": "text/x-python",
   "name": "python",
   "nbconvert_exporter": "python",
   "pygments_lexer": "ipython2",
   "version": "2.7.6"
  },
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